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Rahman was distilling the concept of spatial locality. Prior to optimizing spatial locality in large scale code, Firefox, Chrome, LLVM, Rahman also developed the newest theory on optimal spatial locality in his paper in POPL 2016.

The Computer Science Department Seminar on Monday Nov. 27, 2017 was Automata-Centric Parallelization for Scalable and Parallel Data Processing, given by Prof. Zhijia Zhao of University of Californa, Riverside.

On Oct. 26, Dr. Chengliang Zhang, former graduate and now Staff Software Engineer at Google Seattle, was invited by Chinese Student and Scholar Association (URCSSA) to speak at the second Alumni Summit titled Cloud | Big Data | AI. The compiler group held a separate mini-symposium to present our research and had lunch with our esteemed graduate.

Building new programming languages from whole cloth is a difficult proposition at best. Macro system provide an alternative; they support the construction of new programming languages from existing pieces, while still providing the flexibility to radically change the syntax and semantics of the programming language.

In this talk, I will give a high-level overview of the myriad of programming languages that Racket supports, as well as an overview of the research area of macros, showing what can be accomplished with them and introducing some of the associated technical challenges (and their solutions).

Robby Findler is currently an Associate Professor at Northwestern University, and received his PhD from Rice University in 2002. His research area is programming languages and he focuses on programming environments, software contracts, and tools for modeling operational semantics. He maintains DrRacket, the program development environment for the programming language Racket and he co-authored the book _How to Design Programs_, a textbook for teaching introductory programming.

Redex is a programming language designed to support semantics engineers as they experiment with programming language models. To explore a model, an engineer writes down grammars, type systems, and operational semantics in a notation inspired by the programming languages literature. Redex breathes life into the model, building typing derivations, running example expressions, and using random generation to falsify claims about the model.

This talk gives an overview of Redex, motivating its design choices and giving a sense of how it feels to program in Redex. Then the talk dives into some of the techniques that Redex uses to generate random expressions.

Prof. Gao was the first student since 1970s to leave China to study in MIT computer science 麻省理工计算机专业首位中国大陆留学生. He was a member of the entering class of 1963 at Tsinghua University, and the first from that university to become both ACM and IEEE Fellows.

Abstract

The writing of efficient parallel programs has always been difficult, and is currently compounded by the increasing complexity of architectures. We suggest that these difficulties need to be addressed already at the algorithm design level. Resources such as books for relevant efficient algorithms are currently lacking.

In sequential computing programmers have enjoyed efficiently universal algorithms, which have permitted them to write programs independent of machines. We suggest that for parallel or multi-core computers this will no longer be generally possible. Algorithms that run efficiently will need to be aware of the resource parameters of the machines on which they run. The main promise is that of portable algorithms, those that contain efficient designs for all reasonable ranges of the basic resource parameters and input sizes.

Such portable algorithms need to be designed just once, but, once designed, they can be compiled to run efficiently on any machine. In this way the intellectual effort that goes into parallel algorithms design becomes reusable. To permit such portable algorithms some standard bridging model is needed – a common understanding between hardware and algorithm designers of what the costs of a computation are. We shall describe the Multi-BSP model as a candidate for this role. We show that for several basic problems, namely matrix multiplication, fast Fourier transform, and sorting, portable algorithms do exist that are optimal in a defined sense, for all combinations of input size and parameter values.

Biography

Leslie Valiant was educated at King’s College, Cambridge; Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh.

His work has ranged over several areas of theoretical computer science, particularly complexity theory, computational learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence.

He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, the European Association for Theoretical Computer Science EATCS Award in 2008, and the 2010 A. M. Turing Award. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).